Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies.

Zebrafish have become an essential model organism in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential "normal" behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems.

An in vitro and machine learning framework for quantifying serum albumin binding of per- and polyfluoroalkyl substances.

Per- and polyfluoroalkyl substances (PFAS) are a diverse class of anthropogenic chemicals; many are persistent, bioaccumulative, and mobile in the environment. Worldwide, PFAS bioaccumulation causes serious adverse health impacts, yet the physiochemical determinants of bioaccumulation and toxicity for most PFAS are not well understood, largely due to experimental data deficiencies. As most PFAS are proteinophilic, protein binding is a critical parameter for predicting PFAS bioaccumulation and toxicity. Among these proteins, human serum albumin (HSA) is the predominant blood transport protein for many PFAS. We previously demonstrated the utility of an in vitro differential scanning fluorimetry assay for determining relative HSA binding affinities for 24 PFAS. Here, we report HSA affinities for 65 structurally diverse PFAS from 20 chemical classes. We leverage these experimental data, and chemical/molecular descriptors of PFAS, to build 7 machine learning classifier algorithms and 9 regression algorithms, and evaluate their performance to identify the best predictive binding models. Evaluation of model accuracy revealed that the top performing classifier model, logistic regression, had an AUROC statistic of 0.936. The top performing regression model, support vector regression, had an R2 of 0.854. These top performing models were then used to predict HSA-PFAS binding for chemicals in the EPAPFASINV list of 430 PFAS. These developed in vitro and in silico methodologies represent a high-throughput framework for predicting protein-PFAS binding based on empirical data, and generate directly comparable binding data of potential use in predictive modeling of PFAS bioaccumulation and other toxicokinetic endpoints.

Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies.

Zebrafish have become an essential tool in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential "normal" behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems.

The Effects of Long-term, Low-dose ß-N-methylamino-L-alanine (BMAA) Exposures in Adult SODG93R Transgenic Zebrafish.

ß-N-Methylamino-L-alanine (BMAA) is a non-proteinogenic amino acid produced by cyanobacteria, which has been implicated in several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). It is postulated that chronic exposure to BMAA can lead to formation of protein aggregates, oxidative stress, and/or excitotoxicity, which are mechanisms involved in the etiology of ALS. While specific genetic mutations are identified in some instances of ALS, it is likely that a combination of genetic and environmental factors, such as exposure to the neurotoxin BMAA, contributes to disease. We used a transgenic zebrafish with an ALS-associated mutation, compared with wild-type fish to explore the potential neurotoxic effects of BMAA through chronic long-term exposures. While our results revealed low concentrations of BMAA in the brains of exposed fish, we found no evidence of decreased swim performance or behavioral differences that might be reflective of neurodegenerative disease. Further research is needed to determine if chronic BMAA exposure in adult zebrafish is a suitable model to study neurodegenerative disease initiation and/or progression.

Pharmacogenomic Analyses Implicate B Cell Developmental Status and MKL1 as Determinants of Sensitivity toward Anti-CD20 Monoclonal Antibody Therapy.

Monoclonal antibody (mAb) therapy directed against CD20 is an important tool in the treatment of B cell disorders. However, variable patient response and acquired resistance remain important clinical challenges. To identify genetic factors that may influence sensitivity to treatment, the cytotoxic activity of three CD20 mAbs: rituximab; ofatumumab; and obinutuzumab, were screened in high-throughput assays using 680 ethnically diverse lymphoblastoid cell lines (LCLs) followed by a pharmacogenomic assessment. GWAS analysis identified several novel gene candidates. The most significant SNP, rs58600101, in the gene MKL1 displayed ethnic stratification, with the variant being significantly more prevalent in the African cohort and resulting in reduced transcript levels as measured by qPCR. Functional validation of MKL1 by shRNA-mediated knockdown of MKL1 resulted in a more resistant phenotype. Gene expression analysis identified the developmentally associated TGFB1I1 as the most significant gene associated with sensitivity. qPCR among a panel of sensitive and resistant LCLs revealed immunoglobulin class-switching as well as differences in the expression of B cell activation markers. Flow cytometry showed heterogeneity within some cell lines relative to surface Ig isotype with a shift to more IgG+ cells among the resistant lines. Pretreatment with prednisolone could partly reverse the resistant phenotype. Results suggest that the efficacy of anti-CD20 mAb therapy may be influenced by B cell developmental status as well as polymorphism in the MKL1 gene. A clinical benefit may be achieved by pretreatment with corticosteroids such as prednisolone followed by mAb therapy.

RYK Gene Expression Associated with Drug Response Variation of Temozolomide and Clinical Outcomes in Glioma Patients.

Temozolomide (TMZ) chemotherapy is an important tool in the treatment of glioma brain tumors. However, variable patient response and chemo-resistance remain exceptionally challenging. Our previous genome-wide association study (GWAS) identified a suggestively significant association of SNP rs4470517 in the RYK (receptor-like kinase) gene with TMZ drug response. Functional validation of RYK using lymphocytes and glioma cell lines resulted in gene expression analysis indicating differences in expression status between genotypes of the cell lines and TMZ dose response. We conducted univariate and multivariate Cox regression analyses using publicly available TCGA and GEO datasets to investigate the impact of RYK gene expression status on glioma patient overall (OS) and progression-free survival (PFS). Our results indicated that in IDH mutant gliomas, RYK expression and tumor grade were significant predictors of survival. In IDH wildtype glioblastomas (GBM), MGMT status was the only significant predictor. Despite this result, we revealed a potential benefit of RYK expression in IDH wildtype GBM patients. We found that a combination of RYK expression and MGMT status could serve as an additional biomarker for improved survival. Overall, our findings suggest that RYK expression may serve as an important prognostic or predictor of TMZ response and survival for glioma patients.

MKX-AS1 Gene Expression Associated with Variation in Drug Response to Oxaliplatin and Clinical Outcomes in Colorectal Cancer Patients.

Oxaliplatin (OXAL) is a commonly used chemotherapy for treating colorectal cancer (CRC). A recent genome wide association study (GWAS) showed that a genetic variant (rs11006706) in the lncRNA gene MKX-AS1 and partnered sense gene MKX could impact the response of genetically varied cell lines to OXAL treatment. This study found that the expression levels of MKX-AS1 and MKX in lymphocytes (LCLs) and CRC cell lines differed between the rs11006706 genotypes, indicating that this gene pair could play a role in OXAL response. Further analysis of patient survival data from the Cancer Genome Atlas (TCGA) and other sources showed that patients with high MKX-AS1 expression status had significantly worse overall survival (HR = 3.2; 95%CI = (1.17-9); p = 0.024) compared to cases with low MKX-AS1 expression status. Alternatively, high MKX expression status had significantly better overall survival (HR = 0.22; 95%CI = (0.07-0.7); p = 0.01) compared to cases with low MKX expression status. These results suggest an association between MKX-AS1 and MKX expression status that could be useful as a prognostic marker of response to OXAL and potential patient outcomes in CRC.

Developmental cadmium exposure disrupts zebrafish vestibular calcium channels interfering with otolith formation and inner ear function.

Dizziness or balance problems are estimated to affect approximately 3.3 million children aged three to 17 years. These disorders develop from a breakdown in the balance control system and can be caused by anything that affects the inner ear or the brain, including exposure to environmental toxicants. One potential environmental toxicant linked to balance disorders is cadmium, an extremely toxic metal that occurs naturally in the earth's crust and is released as a byproduct of industrial processes. Cadmium is associated with balance and vestibular dysfunction in adults exposed occupationally, but little is known about the developmental effects of low-concentration cadmium exposure. Our findings indicate that zebrafish exposed to 10-60 parts per billion (ppb) cadmium from four hours post-fertilization (hpf) to seven days post-fertilization (dpf) exhibit abnormal behaviors, including pronounced increases in auditory sensitivity and circling behavior, both of which are linked to reductions in otolith growth and are rescued by the addition of calcium to the media. Pharmacological intervention shows that agonist-induced activation of the P2X calcium ion channel in the presence of cadmium restores otolith size. In conclusion, cadmium-induced ototoxicity is linked to vestibular-based behavioral abnormalities and auditory sensitivity following developmental exposure, and calcium ion channel function is associated with these defects.

LGR5 is a conserved marker of hair follicle stem cells in multiple species and is present early and throughout follicle morphogenesis.

Hair follicle stem cells are key for driving growth and homeostasis of the hair follicle niche, have remarkable regenerative capacity throughout hair cycling, and display fate plasticity during cutaneous wound healing. Due to the need for a transgenic reporter, essentially all observations related to LGR5-expressing hair follicle stem cells have been generated using transgenic mice, which have significant differences in anatomy and physiology from the human. Using a transgenic pig model, a widely accepted model for human skin and human skin repair, we demonstrate that LGR5 is a marker of hair follicle stem cells across species in homeostasis and development. We also report the strong similarities and important differences in expression patterns, gene expression profiles, and developmental processes between species. This information is important for understanding the fundamental differences and similarities across species, and ultimately improving human hair follicle regeneration, cutaneous wound healing, and skin cancer treatment.

Initial experience in staging primary oesophageal/gastro-oesophageal cancer with 18F-FDG PET/MRI.

BACKGROUND: 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) may improve cancer staging by combining sensitive cancer detection with high-contrast resolution and detail. We compared the diagnostic performance of 18F-FDG PET/MRI to 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for staging oesophageal/gastro-oesophageal cancer. Following ethical approval and informed consent, participants with newly diagnosed primary oesophageal/gastro-oesophageal cancer were enrolled. Exclusions included prior/concurrent malignancy. Following 324 ± 28 MBq 18F-FDG administration and 60-min uptake, PET/CT was performed, immediately followed by integrated PET/MRI from skull base to mid-thigh. PET/CT was interpreted by two dual-accredited nuclear medicine physicians and PET/MRI by a dual-accredited nuclear medicine physician/radiologist and cancer radiologist in consensus. Per-participant staging was compared with the tumour board consensus staging using the McNemar test, with statistical significance at 5%. RESULTS: Out of 26 participants, 22 (20 males; mean ± SD age 68.8 ± 8.7 years) completed 18F-FDG PET/CT and PET/MRI. Compared to the tumour board, the primary tumour was staged concordantly in 55% (12/22) with PET/MRI and 36% (8/22) with PET/CT; the nodal stage was concordant in 45% (10/22) with PET/MRI and 50% (11/22) with PET/CT. There was no statistical difference in PET/CT and PET/MRI staging performance (p > 0.05, for T and N staging). The staging of distant metastases was concordant with the tumour board in 95% (21/22) with both PET/MRI and PET/CT. Of participants with distant metastatic disease, PET/MRI detected additional metastases in 30% (3/10). CONCLUSION: In this preliminary study, compared to 18F-FDG PET/CT, 18F-FDG PET/MRI showed non-significant higher concordance with T-staging, but no difference with N or M-staging. Additional metastases detected by 18F-FDG PET/MRI may be of additive clinical value.

Development of a Pandemic Awareness STEM Outreach Curriculum: Utilizing a Computational Thinking Taxonomy Framework.

Computational thinking is an essential skill in the modern global workforce. The current public health crisis has highlighted the need for students and educators to have a deeper understanding of epidemiology. While existing STEM curricula has addressed these topics in the past, current events present an opportunity for new curricula that can be designed to present epidemiology, the science of public health, as a modern topic for students that embeds the problem-solving and mathematics skills of computational thinking practices authentically. Using the Computational Thinking Taxonomy within the informal education setting of a STEM outreach program, a curriculum was developed to introduce middle school students to epidemiological concepts while developing their problem-solving skills, a subset of their computational thinking and mathematical thinking practices, in a contextually rich environment. The informal education setting at a Research I Institution provides avenues to connect diverse learners to visually engaging computational thinking and data science curricula to understand emerging teaching and learning approaches. This paper documents the theory and design approach used by researchers and practitioners to create a Pandemic Awareness STEM Curriculum and future implications for teaching and learning computational thinking practices through engaging with data science.

High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs.

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.

Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.

There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) and computational methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional generative adversarial network (cGAN) or deep neural networks (DNN), and leveraging this large set of toxicity data we could efficiently predict toxic outcomes of untested chemicals. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. In vivo toxicity and chemical data were used to train two neural network generators. The first was a DNN (Go-ZT) while the second utilized cGAN architecture (GAN-ZT) to train generators to produce toxicity data. Our results showed that Go-ZT significantly outperformed the cGAN, support vector machine, random forest and multilayer perceptron models in cross-validation, and when tested against an external test dataset. By combining both Go-ZT and GAN-ZT, our consensus model improved the SE, SP, PPV, and Kappa, to 71.4%, 95.9%, 71.4% and 0.673, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.837. Considering their potential use as prescreening tools, these models could provide in vivo toxicity predictions and insight into the hundreds of thousands of untested chemicals to prioritize compounds for HT testing.

Extending the lymphoblastoid cell line model for drug combination pharmacogenomics.

Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

Longitudinal observation and decline of neutralizing antibody responses in the three months following SARS-CoV-2 infection in humans.

Antibody responses to SARS-CoV-2 can be detected in most infected individuals 10-15 d after the onset of COVID-19 symptoms. However, due to the recent emergence of SARS-CoV-2 in the human population, it is not known how long antibody responses will be maintained or whether they will provide protection from reinfection. Using sequential serum samples collected up to 94 d post onset of symptoms (POS) from 65 individuals with real-time quantitative PCR-confirmed SARS-CoV-2 infection, we show seroconversion (immunoglobulin (Ig)M, IgA, IgG) in >95% of cases and neutralizing antibody responses when sampled beyond 8 d POS. We show that the kinetics of the neutralizing antibody response is typical of an acute viral infection, with declining neutralizing antibody titres observed after an initial peak, and that the magnitude of this peak is dependent on disease severity. Although some individuals with high peak infective dose (ID50 > 10,000) maintained neutralizing antibody titres >1,000 at >60 d POS, some with lower peak ID50 had neutralizing antibody titres approaching baseline within the follow-up period. A similar decline in neutralizing antibody titres was observed in a cohort of 31 seropositive healthcare workers. The present study has important implications when considering widespread serological testing and antibody protection against reinfection with SARS-CoV-2, and may suggest that vaccine boosters are required to provide long-lasting protection.

Is Response Assessment of Breast Cancer Bone Metastases Better with Measurement of 18F-Fluoride Metabolic Flux Than with Measurement of 18F-Fluoride PET/CT SUV?

Our purpose was to establish whether noninvasive measurement of changes in 18F-fluoride metabolic flux to bone mineral (Ki) by PET/CT can provide incremental value in response assessment of bone metastases in breast cancer compared with SUVmax and SUVmeanMethods: Twelve breast cancer patients starting endocrine treatment for de novo or progressive bone metastases were included. Static 18F-fluoride PET/CT scans were acquired 60 min after injection, before and 8 wk after commencing treatment. Venous blood samples were taken at 55 and 85 min after injection to measure plasma 18F-fluoride activity concentrations, and Ki in individual bone metastases was calculated using a previously validated method. Percentage changes in Ki, SUVmax, and SUVmean were calculated from the same index lesions (≤5 lesions) from each patient. Clinical response up to 24 wk, assessed in consensus by 2 experienced oncologists masked to PET imaging findings, was used as a reference standard. Results: Of the 4 patients with clinically progressive disease (PD), mean Ki significantly increased (>25%) in all, SUVmax in 3, and SUVmean in 2. Of the 8 non-PD patients, Ki decreased or remained stable in 7, SUVmax in 5, and SUVmean in 6. A significant mean percentage increase from baseline for Ki, compared with SUVmax and SUVmean, occurred in the 4 patients with PD (89.7% vs. 41.8% and 43.5%, respectively; P < 0.001). Conclusion: After 8 wk of endocrine treatment for bone-predominant metastatic breast cancer, Ki more reliably differentiated PD from non-PD than did SUVmax and SUVmean, probably because measurement of SUV underestimates fluoride clearance by not considering changes in input function.

Prediction of therapy response in bone-predominant metastatic breast cancer: comparison of [18F] fluorodeoxyglucose and [18F]-fluoride PET/CT with whole-body MRI with diffusion-weighted imaging.

PURPOSE: To compare [18F]-fluorodeoxyglucose (FDG) and [18F]-sodium fluoride (NaF) positron emission tomography/computed tomography (PET/CT) with whole-body magnetic resonance with diffusion-weighted imaging (WB-MRI), for endocrine therapy response prediction at 8 weeks in bone-predominant metastatic breast cancer. PATIENTS AND METHODS: Thirty-one patients scheduled for endocrine therapy had up to five bone metastases measured [FDG, NaF PET/CT: maximum standardized uptake value (SUVmax); WB-MRI: median apparent diffusion coefficient (ADCmed)] at baseline and 8 weeks. To detect the flare phenomenon, a 12-week NaF PET/CT was also performed if 8-week SUVmax increased. A 25% parameter change differentiated imaging progressive disease (PD) from non-PD and was compared to a 24-week clinical reference standard and progression-free survival (PFS). RESULTS: Twenty-two patients (median age, 58.6 years, range, 40-79 years) completing baseline and 8-week imaging were included in the final analysis. Per-patient % change in NaF SUVmax predicted 24-week clinical PD with sensitivity, specificity and accuracy of 60, 73.3, and 70%, respectively. For FDG SUVmax the results were 0, 100, and 76.2% and for ADCmed, 0, 100 and 72.2%, respectively. PFS < 24 weeks was associated with % change in SUVmax (NaF: 41.7 vs. 0.7%, p = 0.039; FDG: - 4.8 vs. - 28.6%, p = 0.005) but not ADCmed (- 0.5 vs. 10.1%, p = 0.098). Interlesional response heterogeneity occurred in all modalities and NaF flare occurred in seven patients. CONCLUSIONS: FDG PET/CT and WB-MRI best predicted clinical non-PD and both FDG and NaF PET/CT predicted PFS < 24 weeks. Lesional response heterogeneity occurs with all modalities and flare is common with NaF PET/CT.

Cadmium exposure increases the risk of juvenile obesity: a human and zebrafish comparative study.

OBJECTIVE: Human obesity is a complex metabolic disorder disproportionately affecting people of lower socioeconomic strata, and ethnic minorities, especially African Americans and Hispanics. Although genetic predisposition and a positive energy balance are implicated in obesity, these factors alone do not account for the excess prevalence of obesity in lower socioeconomic populations. Therefore, environmental factors, including exposure to pesticides, heavy metals, and other contaminants, are agents widely suspected to have obesogenic activity, and they also are spatially correlated with lower socioeconomic status. Our study investigates the causal relationship between exposure to the heavy metal, cadmium (Cd), and obesity in a cohort of children and in a zebrafish model of adipogenesis. DESIGN: An extensive collection of first trimester maternal blood samples obtained as part of the Newborn Epigenetics Study (NEST) was analyzed for the presence of Cd, and these results were cross analyzed with the weight-gain trajectory of the children through age 5 years. Next, the role of Cd as a potential obesogen was analyzed in an in vivo zebrafish model. RESULTS: Our analysis indicates that the presence of Cd in maternal blood during pregnancy is associated with increased risk of juvenile obesity in the offspring, independent of other variables, including lead (Pb) and smoking status. Our results are recapitulated in a zebrafish model, in which exposure to Cd at levels approximating those observed in the NEST study is associated with increased adiposity. CONCLUSION: Our findings identify Cd as a potential human obesogen. Moreover, these observations are recapitulated in a zebrafish model, suggesting that the underlying mechanisms may be evolutionarily conserved, and that zebrafish may be a valuable model for uncovering pathways leading to Cd-mediated obesity in human populations.